1. Supporting Molecular Simulation-based Bio/Nano Research on Computational GRIDs Karpjoo Jeong (jeongk@konkuk.ac.kr), Konkuk Univ.jeongk@konkuk.ac.kr Suntae Hwang (sthwang@kookmin.ac.kr), Kookmin Univ.sthwang@kookmin.ac.kr

2. 2 Collaboration IT – Karpjoo Jeong at Konkuk University – Suntae Hwang at Kookmin University – Younghwan Park at Hansung University BT/NT – Seunho Jung at Konkuk University – Yoongho Im at Konkuk University

3. 3 Contents Molecular Simulation-based Bio  Nano Research How to Build Cheap but Powerful Supercomputer How to Manage Lots of Simulation Results from Supercomputers Molecular Simulation System on World-wide Computational GRIDs Implementation Status and Preliminary Performance Results Conclusions and Future Work

4. 4 Molecular Simulation-based Bio  Nano Research

5. 5 Characteristics Requirements for very large computation Complicated research process in a workflow style – Consist of Modeling, Simulation, Verification Tasks which form a complex workflow Credibility of simulation tool is crucial – A few well-known software packages are only accepted Lots of repetition of same simulation and application to similar problems – But with different parameters

6. 6 Characteristics (Cont ’ d) Good News – Lots of parallelism in research tasks – No need for writing complicated simulation code (in most cases) Bad News – Frequent scientists ’ intervention is required Verify intermediate results Guide simulation directions – Single task (single instance of simulation execution) alone may be very large – Parallel simulation is extremely difficult

7. 7 Potentials for GRID Inter-Task Independence and Parallelism Problem-level Parallelism – Most coarse-grained – Solving similar problems by similar methods Simulation-level Parallelism – Coarse-grained – Repetition of same simulation but with different parameters

8. 8 How to Build Cheap and Powerful Supercomputer

9. 9 PC Lab-based Virtual Parallel Computers Goals – Utilize idle computing resources at many PC labs, universities around the world Hundreds or thousands of PCs at each university which are almost 100% idle at night Relatively less sensitive to security issues – Build these PCs into virtual parallel computers a thousand of Pentium4 2.0GHz CPUs can match very expensive supercomputers – Apply these parallel computers for coarse-grained parallel problems such as molecular simulation- based bio/nano research problems

10. 10 Vision: World-wide Computing Night-time workDay-time Work Migration Project1 Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Project1 Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation

11. 11 Base Computing System Persistent Linda Parallel/Distributed System Linda Parallel Programming Model – Shared Memory Model in Mailbox Style – Ease of programming Heterogeneity Support (Ex, Linux 및 MS Windows) – Process Migration – Parallel Computation Migration Fault Tolerance IDLE PC Utilization Efficient Support for Coarse-grained Parallel Computation

12. 12 Prototype PC Lab at College of Information and Communication, Konkuk University, Seoul, Korea 50 Pentium4 PCs Linux cluster with 5 nodes

13. 13 Web Monitoring Interface

14. 14 How to Manage Lots of Simulation Results from Supercomputers

15. 15 Workflow Approach Provide workflow-based simulation environment – Allow scientists to plan research processes in a workflow style – Manage intermediate results and notify scientists of next tasks – Execute independent tasks in parallel Scientists can avoid tedious management overheads and focus on planning, analysis and verification work

16. 16 GRID-based Molecular Simulation Environment Workflow-based simulation environment submits simulation tasks to computational GRIDs in a user-transparent way Computational GRIDs Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Computational GRIDs Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation

17. 17 Computational GRIDs Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Computational GRIDs Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation

18. 18 Molecular Simulation System on World-wide Computation GRIDs (Persistent Linda and Globus)

19. 19 Persistent Linda Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Persistent Linda Shared computing resources CHARMM, AMBER Tasks Numerous independent tasks Gateway Agent GRAM GridFTP Ex. Konkuk Univ. Ex. KISTI Data/Result Files Task Request Molecular Simulation GLOBUS Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation

20. 20 Fault Tolerance and Migration Simulation packages such as CHARM and GAUSSIAN support checkpointing facilities – Save computation status to disk and resume computation from it later Our Molecular Simulation System is designed to use these facilities to deal with fault tolerance and migration Checkpointing is a solution to long-running simulation

21. 21 Persistent Linda Persistent Linda Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Persistent Linda Persistent Linda GLOBUS Workflow-based Simulation Environment Project1 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation Prject2 Molecular Simulation Molecular Simulation Molecular Simulation Molecular Simulation

22. 22 Implementation Status and Preliminary Performance Results

23. 23 Implementation Status Persistent Linda System – Used for various parallel applications for years – Recently ported to MS Windows Workflow Molecular Simulation System – Implementation of prototype is underway Globus-based global coordination middleware – Gateway between Persistent Linda and Globus is implemented – Global scheduler is being designed

24. 24 Experiments Settings – Simple client on remote host – Persistent Linda System – Three CHARM driver programs on three Linux servers (P4 2.0Ghz) which invoke CHARM Scenario: remote invocation of single CHARM task Result – about 30 seconds for remote invocation overhead Persistent Linda CHARMM Driver Linux Server CHARMM Driver Linux Server CHARMM Driver Linux Server GRAM GridFTP Gateway agent CHARMM agent charmm job charmm result Remote Site

25. 25 Conclusions and Future Work Propose molecular simulation system on computational GRIDs – Utilize idle PCs at university Labs – Workflow-based simulation environment Effective for coarse-grained parallel problems such as molecular simulation-based bio/nano research Developing Globus-based global middleware Planning on – Large scale computational GRIDs by combining several university labs – Application for bio/nano research Database for chiral molecules